Reflection seismic random noise attenuation using non-linear diffusion tensor anisotropic filtering
Abstract:
Summary Reflection seismology is one the exploration geophysical methods which uses the principles of seismology to estimate the properties of the earth subsurface from reflected seismic waves. It is a type of geophysical imaging technique used to image the subsurface of the earth and understand its geology. Seismic data acquisition always is accompanied by variety of noises. Therefore, improvement of the signal-to-noise ratio (SNR) of seismic data is a sticky thread in the processing of seismic data for high quality imaging. Many efforts have been made to remove this type of noise from seismic data and several authors have suggested different methods for this purpose. Each of these methods has its own advantages and disadvantages. In this paper, we have used non-linear diffusion filter to attenuate the reflection seismic random noise. The results pointed out in this paper show significant improvement of SNR through enhanced reflector continuity for a better interpretation. They show that the diffusion filter produces acceptable results compared to the well-known f-x deconvolution and median filter.
Introduction Seismic data are affected by various types of noises such as ground rolls, multiples and etc. Generally, seismic noises are divided into two categories: coherent and incoherent. Unlike the coherent noise, incoherent noise known as random noise, is not correlated from trace to trace. Random noise resulted from random oscillation during acquiring data is one of the most important and harmful noises that exist in seismic data over entire time and frequency. Therefore, improvement of the signal-to-noise ratio (SNR) of seismic data is a sticky thread in the processing of seismic data for high quality imaging. Many efforts have been made to remove this type of noise from seismic data and several authors have suggested different methods, such as f-x filtering, polynomial fitting, and singular value decomposition, to remove random noise from seismic data. Each of these methods has its own advantages and disadvantages. A significant part of the seismic random noise can be attenuated by stacking procedure, but part of random noise also remains after stacking. Attenuation of remaining part of noise requires an advanced method. Smoothing filter is one of the most efficient and traditional type of random noise suppression in signal and images. Diffusion filter is one of the most important smoothing filters, which is based on a physical concept named as diffusion.
Methodology and Approaches The diffusion defines a physical procedure for balancing concentration changes without creating or destroying mass. The analogy with image processing can be drawn if we consider the concentration as the intensity of the image. The diffusion coefficient or diffusivity function, which adaptively controls the smoothing amount is one of the most effective parameters in diffusion process. If the diffusion coefficient is selected as a constant value, the diffusion process will be linear. In other words, the smoothing procedure will be performed as homogeneous and isotropic and causes the output image to be blurry. If the diffusion coefficient is a function of image gradient, the diffusion process will be scalar. In these circumstances, the smoothing procedure will be performed as inhomogeneous and isotropic. Scalar diffusion filter has better efficiency than the linear form; however, it has not good performance in the edge position due to its isotropic property. Anisotropic non-linear diffusion filter is a proper tool to filter images while preserving details and even enhancing edges. The diffusion coefficient of an anisotropic non-linear diffusion filter is defined as tensor. The basic idea of the anisotropic non-linear diffusion filter is to preserve edges. This is attained by orienting the diffusion process parallel to the edge.
Results and Conclusions The anisotropic non-linear diffusion filter is a powerful method for image de-noising and edge preserving. The efficiency of the diffusion filter was tested on both real and synthetic seismic data. The method effectively recovered seismic signals on noisy synthetic and field seismic data. The obtained results were compared with the results of the well-known f-x deconvolution and median filter. After analyzing all of the results, the anisotropic non-linear diffusion filter proved to be an alternative algorithm for seismic data de-noising and had the best performance among the other three filter methods.
Language:
Persian
Published:
Journal Of Research on Applied Geophysics, Volume:1 Issue: 2, 2016
Pages:
105 to 118
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